#################################################################################
##### INF889E - Méthodes d'intelligence artificielle en bioinformatique #####
##### Classification de VIH par zones géographiques #####
#################################################################################
##### Author: Riccardo Z****** #####
##### This program is partly inspired by the work presented in a class #####
##### workshop by Dylan Lebatteux. #####
#################################################################################
# Import functions
import re
import joblib
import numpy as np
import pandas as pd
from os import listdir
from random import shuffle
from progressbar import ProgressBar
from Bio import SeqIO, pairwise2
from Bio.motifs import create
from sklearn import svm
from sklearn.preprocessing import MinMaxScaler
from sklearn.feature_selection import RFE
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.manifold import TSNE
from sklearn.decomposition import PCA
from mpl_toolkits.mplot3d import Axes3D
from dna_features_viewer import GraphicFeature, GraphicRecord
import matplotlib.pyplot as plt
import seaborn as sns
##############################################
##### IMPORTANT VARIABLES #####
##############################################
# Scope of classification: if "ALL", classify by region globaly
# If AFR, ASI, CAM, CRB, EUR, FSU, MEA, NAM, OCE or SAM, classify by country within this chosen region
scope = "ALL"
# Access path for the FASTA files (one file for each region)
path = "../../../../data/" + "complete"
# Name of trained model when saving
model_name = "k-mers-4.pkl"
# For sampling purposes: will process max n sequences for each target class
n_samples = 250000
# Classification features as sum (false) of motifs or as frequency (true) of motifs
freq = False
# Elimination step for features selection
step = 5
# Number of features to select
n_features = 100
# Train / Test split ratio
split_raito = 0.8
# Dimensions for graphs (2D or 3D)
n_components = 2
# Set maximum number of incorrect records to analyse at the end
max_incorrect = 10
# Set maximum number of correct records to compute alignment with at the end
max_correct = 1000
# Set the length k of the features based on k-mers
k = 4
##############################################
##### DATA INITIALISATION #####
##############################################
print("\n DATA INITIALISATION ")
print("=====================================")
DATA INITIALISATION
=====================================
# Will contain all the data rows, in the form of biopython seq_records objects
data = []
# Will contain a pair of target class -> number of data rows with this target class
targets = {}
# Process raw record label information into its annotations, then insert it into data
# To update if the label of sequences in the FASTA files changes
def add_record(record, target):
# Initialiation of the seq_record
header = record.id.split(".")
record.id = header[4]
record.name = header[3]
record.seq = record.seq.upper()
record.annotations = {"target": target, "subtype": header[0], "country": header[1]}
# Add it to the data table and update the target classes dictionary
targets[target] = targets.get(target, 0) + 1
data.append(record)
# Properly fills the data table using the above function
if scope == "ALL":
# Used to show progress
progress = ProgressBar()
# If scope is ALL, each filename is the name of each region used as a target class
for filename in progress(listdir(path)):
target = filename.split('.')[0]
for record in SeqIO.parse(path + "/" + filename, "fasta"):
add_record(record, target)
print("")
else:
# Else, countries are target classes, and the scope region is the filename
for record in SeqIO.parse(path + "/" + scope + ".fasta", "fasta"):
target = record.id.split(".")[1]
add_record(record, target)
100% (9 of 9) |##########################| Elapsed Time: 0:00:01 Time: 0:00:01
# Dipslay data information
print("Data information:")
print("Number of sequences:", sum(targets.values()))
print("Number of targets:", len(targets))
print("Minimum number of instances:", min(targets.values()))
print("Maximum number of instances:", max(targets.values()))
# Dipslay data summary
print("\nData summary:")
for key, value in targets.items():
print("Target:", key, "| Number of sequences:", value)
# Display the first 5 samples
print("\nInformation of the first 5 samples:")
for i in range(5):
print("ID:", data[i].id, "| Sequence:", data[i].seq[0:50], "| Annotations:", data[i].annotations)
Data information:
Number of sequences: 13916
Number of targets: 9
Minimum number of instances: 40
Maximum number of instances: 5223
Data summary:
Target: AFR | Number of sequences: 3377
Target: ASI | Number of sequences: 2559
Target: CRB | Number of sequences: 40
Target: EUR | Number of sequences: 1783
Target: FUS | Number of sequences: 235
Target: MEA | Number of sequences: 41
Target: NAM | Number of sequences: 5223
Target: OCE | Number of sequences: 40
Target: SAM | Number of sequences: 618
Information of the first 5 samples:
ID: AB049811 | Sequence: TGGATGGGCTAATTTACTCCAAGAAAAGACAAGAGATCCTTGATCTGTGG | Annotations: {'target': 'AFR', 'subtype': '02_AG', 'country': 'GH'}
ID: AB052867 | Sequence: TGGAAGGGTTAATTCATTCCCAGAAAAGACAAGACATCCTTGATCTGTGG | Annotations: {'target': 'AFR', 'subtype': '02A1', 'country': 'GH'}
ID: AB098330 | Sequence: TGGATGGGTTAATTTACTCCAGGAAAAGACAGGAAATCCTTGATCTGTGG | Annotations: {'target': 'AFR', 'subtype': 'A1', 'country': 'UG'}
ID: AB098331 | Sequence: TGGATGGGTTAATTTACTCCAGGAAAAGACAGGAAATCCTTGATCTGTGG | Annotations: {'target': 'AFR', 'subtype': 'A1', 'country': 'UG'}
ID: AB098332 | Sequence: TGGATGGGTTAATTTACTCCAGGAAAAGACAAGAAATCCTTGATCTGTGG | Annotations: {'target': 'AFR', 'subtype': 'A1', 'country': 'UG'}
##############################################
##### TRAIN / TEST DATA SPLIT #####
##############################################
# Initialise train/test tables that will contain the data
train_data = []
test_data = []
# Initialise train/test dictionaries that will contain the number of instances for each target
test_split = {}
train_split = {}
# Initialise the dictionary with the targets keys and the value 0
test_split = test_split.fromkeys(targets.keys(), 0)
train_split = train_split.fromkeys(targets.keys(), 0)
# Shuffle the data
shuffle(data)
# Iterate through the data
for d in data:
# Get this records's target class
target = d.annotations["target"]
# For sampling purposes: train/test threshold is based on n_samples if there is too much records for this target
threshold = min(targets[target], n_samples) * split_raito
# Until threshold for this target is reached, fills train data
if train_split[target] < threshold:
train_data.append(d)
train_split[target] += 1
# Then, fills test data (until eventually n_samples are collected)
elif test_split[target] < n_samples * (1-split_raito):
test_data.append(d)
test_split[target] += 1
# Shuffle the data
shuffle(train_data)
shuffle(test_data)
# Data summary of the train/test split
print("\nTrain/Test split summary:")
for train_key, test_key in zip(train_split.keys(), test_split.keys()):
print("Target:", train_key, "| Train instances:", train_split[train_key], "| Test instances:", test_split[test_key])
print("\nTotal number of training instances:", len(train_data))
print("Total number of testing instances:", len(test_data))
Train/Test split summary: Target: AFR | Train instances: 2702 | Test instances: 675 Target: ASI | Train instances: 2048 | Test instances: 511 Target: CRB | Train instances: 32 | Test instances: 8 Target: EUR | Train instances: 1427 | Test instances: 356 Target: FUS | Train instances: 188 | Test instances: 47 Target: MEA | Train instances: 33 | Test instances: 8 Target: NAM | Train instances: 4179 | Test instances: 1044 Target: OCE | Train instances: 32 | Test instances: 8 Target: SAM | Train instances: 495 | Test instances: 123 Total number of training instances: 11136 Total number of testing instances: 2780
##################################################
##### FEATURES GENERATION BASED ON K-MERS #####
##################################################
print("\n FEATURES GENERATION ")
print("=====================================")
FEATURES GENERATION
=====================================
# Initialize an empty dictionary for the k-mers motifs features
instances = {}
# Used to show progress
progress = ProgressBar()
# Iterate through the training data
for d in train_data:
# Go through the sequence
for i in range(0, len(d.seq) - k + 1, 1):
# Get the current k-mer motif feature
feature = str(d.seq[i:i + k])
# If it contains only the characters "A", "C", "G" or "T", it will be saved
if re.match('^[ACGT]+$', feature):
instances[feature] = 0
progress.update(len(instances))
# No need to keep going if motifs dictonary reaches max size
if len(instances) == 4 ** k:
break
# Used to show progress
progress.finish()
# Save dictonary keys as biopython motifs object
motifs = create(instances.keys())
# Display the number of features
print("\nNumber of features:", len(motifs.instances), "\n")
| |# | 256 Elapsed Time: 0:00:00 Number of features: 256
######################################################################
##### GENERATION OF THE FEATURE MATRIX (x) AND TARGET VECTOR (y) #####
######################################################################
# Function to generate feature matrix and target vector
def generateFeatures(data):
# Initialize the feature matrix
X = []
# Initialize the target vector
y = []
# Used to show progress
progress = ProgressBar()
# Iterate through the data
for d in progress(data):
# Generate an empty dictionary
x = {}
# Initialize the dictionary with targets as keys and 0 as value
x = x.fromkeys(motifs.instances, 0)
# Compute X (features matrix): the number of occurrence of k-mers (with overlaping)
for i in range(0, len(d.seq) - k + 1, 1):
feature = d.seq[i:i + k]
# Attempt to increment the number of occurrences of the current k-mer feature
try: x[feature] += 1
# It could fail because the current k-mer is not full ACGT
except: pass
# Save the features vector in the features matrix
X.append(list(x.values()))
# Save the target class in the target vector
y.append(d.annotations["target"])
# Return matrices X and y (feature matrix and target vector)
return X, y
# Generate train/test feature matrices and target vectors
x_train, y_train = generateFeatures(train_data)
x_test, y_test = generateFeatures(test_data)
100% (11136 of 11136) |##################| Elapsed Time: 0:05:06 Time: 0:05:06 100% (2780 of 2780) |####################| Elapsed Time: 0:01:31 Time: 0:01:31
# Function to generate feature matrix and target vector based on k-mer frequency, not the sum
def generateFreqFeatures(x_sum):
X = []
for x in x_sum:
total = sum(x)
X.append(list(map((lambda i: i / total), x)))
return X
# If Freq is ture, then the features matrix are frequency of k-mers, not their sum
if freq:
x_train = generateFreqFeatures(x_train)
x_test = generateFreqFeatures(x_test)
##############################################
##### FEATURES NORMALISATION #####
##############################################
# Instantiate a MinMaxScaler between 0 and 1
minMaxScaler = MinMaxScaler(feature_range = (0,1))
# Apply a scaling to the train and test set
x_train = minMaxScaler.fit_transform(x_train)
x_test = minMaxScaler.fit_transform(x_test)
##############################################
##### FEATURES SELECTION #####
##############################################
print("\n FEATURES SELECTION ")
print("=====================================")
FEATURES SELECTION
=====================================
# Instantiate a linear model based on svm
model = svm.SVC(C = 1.0, kernel='linear', class_weight = None)
# Instantiate the RFE
rfe = RFE(model, n_features_to_select = n_features, step = step, verbose=True)
# Apply RFE and transform the training matrix
x_train = rfe.fit_transform(x_train, y_train)
# Tranform the test matrix (will be useed later for evaluation purposes)
x_test = rfe.transform(x_test)
Fitting estimator with 256 features. Fitting estimator with 251 features. Fitting estimator with 246 features. Fitting estimator with 241 features. Fitting estimator with 236 features. Fitting estimator with 231 features. Fitting estimator with 226 features. Fitting estimator with 221 features. Fitting estimator with 216 features. Fitting estimator with 211 features. Fitting estimator with 206 features. Fitting estimator with 201 features. Fitting estimator with 196 features. Fitting estimator with 191 features. Fitting estimator with 186 features. Fitting estimator with 181 features. Fitting estimator with 176 features. Fitting estimator with 171 features. Fitting estimator with 166 features. Fitting estimator with 161 features. Fitting estimator with 156 features. Fitting estimator with 151 features. Fitting estimator with 146 features. Fitting estimator with 141 features. Fitting estimator with 136 features. Fitting estimator with 131 features. Fitting estimator with 126 features. Fitting estimator with 121 features. Fitting estimator with 116 features. Fitting estimator with 111 features. Fitting estimator with 106 features. Fitting estimator with 101 features.
# Compute the reduction percentage of the feature matrix
reduction_percentage = ((len(motifs.instances) - n_features) / len(motifs.instances) * 100)
# Print the reduction percentage
print("\nReduction percentage:", round(reduction_percentage, 2), "%")
Reduction percentage: 60.94 %
# Initialize the table that will contain the selected features
instances = []
# Save selected k-mers features
for i, mask in enumerate(rfe.support_):
if mask == True: instances.append(motifs.instances[i])
# Save table as biopython motifs object
features = create(instances)
##############################################
##### TRAINING DATA VISUALISATION #####
##############################################
print("\n TRAINING DATA VISUALISATION ")
print("=====================================")
TRAINING DATA VISUALISATION
=====================================
# Define the function to draw Scatter Plot
def generateScatterPlot(title, figure_width, figure_height, data, X, y):
# If 2d dimensions
if n_components == 2:
# Initialize a 2-dimensional figure
fig, ax = plt.subplots(figsize=(figure_width, figure_height))
# If 3d dimensions
else:
# Initialize a 3-dimensional figure
fig = plt.figure(figsize=(15, 10))
ax = Axes3D(fig)
# List of markers
markers = ["o","+", "^", "x"]
# List of colors
colors = ["tab:blue", "tab:orange",
"tab:green", "tab:red",
"tab:purple", "tab:brown",
"tab:pink", "tab:grey",
"tab:olive", "tab:cyan",]
# Iterate through the targets
for i, target in enumerate(y):
# Set the list of axis positions
x = []
y = []
z = []
# If the number of targets is less than 10
if i < 10:
color = colors[i]
marker = markers[0]
# If the number of targets is less than 20
elif i < 20:
color = colors[i-10]
marker = markers[1]
# If the number of targets is less than 30
elif i < 30:
color = colors[i-20]
marker = markers[2]
# If the number of targets is less than 40
else:
color = colors[i-30]
marker = markers[3]
# Iterate through the data
for i, d in enumerate(data):
# If the sequence belongs to the target of interest
if d.annotations["target"] == target:
# Save the value of the positions
x.append(X[i][0])
y.append(X[i][1])
if n_components == 3: z.append(X[i][2])
# Add the current scatter plot to the figure
if n_components == 2:
ax.scatter(x, y, c = color, label = target, alpha = 0.75, edgecolors = 'none', marker=marker)
else:
ax.scatter(x, y, z, c = color, label=target,alpha=0.75, edgecolors='none', marker=marker)
# Display the grid
ax.grid(True)
# Set the legend parameters
ax.legend(loc = 2, prop = {'size': 10})
# Set the tite
plt.title(title)
# Set axes labels
if n_components == 2:
plt.xlabel('PC1')
plt.ylabel('PC2')
else:
ax.set_xlabel('PC1')
ax.set_ylabel('PC2')
ax.set_zlabel('PC3')
# Displqy the figure
plt.show()
# Instantiate a TSNE with 3 principal components
tsne = TSNE(n_components = 3, perplexity = 50, verbose=True)
# Apply TSNE to X_train
x_tsne = tsne.fit_transform(x_train)
[t-SNE] Computing 151 nearest neighbors... [t-SNE] Indexed 11136 samples in 0.005s... [t-SNE] Computed neighbors for 11136 samples in 7.413s... [t-SNE] Computed conditional probabilities for sample 1000 / 11136 [t-SNE] Computed conditional probabilities for sample 2000 / 11136 [t-SNE] Computed conditional probabilities for sample 3000 / 11136 [t-SNE] Computed conditional probabilities for sample 4000 / 11136 [t-SNE] Computed conditional probabilities for sample 5000 / 11136 [t-SNE] Computed conditional probabilities for sample 6000 / 11136 [t-SNE] Computed conditional probabilities for sample 7000 / 11136 [t-SNE] Computed conditional probabilities for sample 8000 / 11136 [t-SNE] Computed conditional probabilities for sample 9000 / 11136 [t-SNE] Computed conditional probabilities for sample 10000 / 11136 [t-SNE] Computed conditional probabilities for sample 11000 / 11136 [t-SNE] Computed conditional probabilities for sample 11136 / 11136 [t-SNE] Mean sigma: 0.102731 [t-SNE] KL divergence after 250 iterations with early exaggeration: 79.407158 [t-SNE] KL divergence after 1000 iterations: 1.053715
# Generate scatter plot of a TSNE
generateScatterPlot(title= "Scatter plot of a two-dimensional TSNE applied to the training data",
figure_width = 15,
figure_height = 12,
data = train_data,
X = x_tsne,
y = set(y_train))
# Instantiate PCA with 3 principal components
pca = PCA(n_components = 3)
x_pca = pca.fit_transform(x_train)
# Generate scatter plot of a PCA
generateScatterPlot(title= "Scatter plot of a two-dimensional PCA applied to the training data",
figure_width = 15,
figure_height = 12,
data = train_data,
X = x_pca,
y = set(y_train))
##############################################
##### MODEL TRAINING AND PREDICTION #####
##############################################
print("\n MODEL TRAINING AND PREDICTION ")
print("=====================================")
MODEL TRAINING AND PREDICTION
=====================================
# Fit the model on the train set
model.fit(x_train, y_train)
# Save the model to filename model_name
joblib.dump(model, model_name)
['k-mers-4.pkl']
# Predict with model on the test set
y_pred = model.predict(x_test)
# Display prediction
print("Predictions (" + str(len(y_pred)) + "):", y_pred)
Predictions (2780): ['NAM' 'AFR' 'EUR' ... 'EUR' 'SAM' 'NAM']
##############################################
##### MODEL PREDICTIONS VISUALISATION #####
##############################################
print("\n MODEL PREDICTIONS VISUALISATION ")
print("=====================================")
MODEL PREDICTIONS VISUALISATION =====================================
# Will contain correct and incorrect data seq_records objects
correct_data = []
incorrect_data = []
# Will contain correct and incorrect features vectors (just like x_test)
correct_features = []
incorrect_features = []
# Iterate through test data
for i, d in enumerate(test_data):
# Add an annotation to all test data stating its percentage range of ACGT characters
total_char = len(d.seq)
total_acgt = 0
for char in d.seq:
if re.match('^[ACGT]+$', char):
total_acgt += 1
acgt_percent = total_acgt / total_char
if acgt_percent >= 0.75: d.annotations["acgt-percent"] = "75-100"
elif acgt_percent >= 0.50: d.annotations["acgt-percent"] = "50-75"
elif acgt_percent >= 0.25: d.annotations["acgt-percent"] = "25-50"
else: d.annotations["acgt-percent"] = "0-25"
# Split test data into correct and incorrect sets depending on prediction results
if y_pred[i] == d.annotations["target"]:
correct_data.append(d)
correct_features.append(x_test[i])
else:
# If it's incorrect, add the prediction class as an annotation
d.annotations["prediction"] = y_pred[i]
incorrect_data.append(d)
incorrect_features.append(x_test[i])
# Print the classification_report
print(classification_report(y_test, y_pred, digits = 3))
precision recall f1-score support
AFR 0.886 0.877 0.882 675
ASI 0.971 0.798 0.876 511
CRB 0.429 0.375 0.400 8
EUR 0.678 0.539 0.601 356
FUS 0.488 0.872 0.626 47
MEA 1.000 0.500 0.667 8
NAM 0.869 0.949 0.907 1044
OCE 0.000 0.000 0.000 8
SAM 0.503 0.707 0.588 123
accuracy 0.834 2780
macro avg 0.647 0.624 0.616 2780
weighted avg 0.841 0.834 0.832 2780
C:\Users\Riccardo\AppData\Roaming\Python\Python39\site-packages\sklearn\metrics\_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
C:\Users\Riccardo\AppData\Roaming\Python\Python39\site-packages\sklearn\metrics\_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
C:\Users\Riccardo\AppData\Roaming\Python\Python39\site-packages\sklearn\metrics\_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
# Dictonaries with pair of annotation -> number of incorrect records with this annotation
subtypes = {}
countries = {}
predictions = {}
acgt_percents = {}
# Iterate through incorrect data
for i in incorrect_data:
# Increment each kind of annotation with current record values as keys
subtypes[i.annotations["subtype"]] = subtypes.get(i.annotations["subtype"], 0) + 1
countries[i.annotations["country"]] = countries.get(i.annotations["country"], 0) + 1
predictions[i.annotations["prediction"]] = predictions.get(i.annotations["prediction"], 0) + 1
acgt_percents[i.annotations["acgt-percent"]] = acgt_percents.get(i.annotations["acgt-percent"], 0) + 1
# Display number of incorrect records for each annotation, useful to spot any pattern here
print("Incorrect predictions annotations:")
print("Subtype:", subtypes)
print("Country:", countries)
print("Prediction:", predictions)
print("ACGT percent:", acgt_percents)
Incorrect predictions annotations:
Subtype: {'B': 210, 'G': 13, 'U': 5, '02_AG': 19, '20_BG': 2, 'BF1': 10, '13_cpx': 1, 'A1': 6, 'O': 9, 'C': 52, '01B': 6, 'BF1G': 1, 'A6': 6, '01_AE': 8, '03A': 1, '02F2U': 1, 'CD': 2, '50B': 1, '85_BC': 1, 'AD': 2, 'BC': 8, 'DG': 1, '06G': 1, 'A1C': 4, '114_01B': 1, '02U': 1, '18_cpx': 2, '43_02G': 2, '24_BG': 1, 'BG': 2, '45_cpx': 2, '14_BG': 3, 'DO': 1, '90_BF1': 1, '94_cpx': 1, 'F1': 11, 'A1D': 2, 'A1B': 2, '02A1': 6, 'AB': 1, '108_BC': 2, '-': 7, '36_cpx': 1, '92_C2U': 1, '95_02B': 1, 'A': 1, '11_cpx': 3, '0122F': 1, 'F2': 1, 'GK': 1, 'CU': 1, '27_cpx': 2, 'P': 1, '25_cpx': 1, 'F2O': 2, '02G': 1, 'A1BD': 1, 'BCF1': 1, 'D': 3, '47_BF': 2, '31_BC': 1, '04_cpx': 1, 'A3': 1, 'N': 1, '02B': 1, '35_AD': 1, '0206': 2, '0107': 1, '02AG': 1, '89_BF': 1, '01C': 1, '71_BF1': 1, '02A': 2, 'J': 1, 'A1O': 1, '60_BC': 1, '02A6': 1}
Country: {'KR': 26, 'GW': 4, 'SI': 4, 'US': 52, 'CU': 4, 'NL': 3, 'JP': 13, 'CM': 24, 'RW': 2, 'SE': 21, 'BE': 23, 'CY': 13, 'MO': 1, 'IT': 3, 'TH': 13, 'PK': 7, 'FR': 13, 'ZA': 13, 'ES': 43, 'CN': 24, 'GB': 21, 'IN': 11, 'ET': 1, 'EE': 1, 'KE': 6, 'NG': 7, 'AU': 8, 'RU': 2, 'CH': 6, 'UY': 3, 'DE': 9, 'CO': 1, 'CD': 6, 'AR': 6, 'BR': 21, 'GA': 2, 'ID': 2, 'IL': 2, 'NP': 3, 'PE': 4, 'AO': 2, 'BW': 2, 'HK': 1, 'CF': 2, 'CI': 1, 'ZM': 1, 'MW': 4, 'CA': 1, 'DK': 2, 'UA': 3, 'TW': 1, 'GR': 1, 'BG': 1, 'SN': 1, 'GH': 1, 'UG': 2, 'AF': 1, 'GE': 1, 'NE': 1, 'SA': 1, 'DO': 1, 'BJ': 1, 'BO': 1, 'MY': 1}
Prediction: {'NAM': 150, 'FUS': 43, 'AFR': 76, 'EUR': 91, 'SAM': 86, 'CRB': 4, 'ASI': 12}
ACGT percent: {'75-100': 462}
# Compute the confusion matrix
matrix = confusion_matrix(y_true = y_test, y_pred = y_pred)
# Build the heatmap
fig, ax = plt.subplots(figsize=(15, 10))
sns.heatmap(matrix,
cmap = 'Blues',
annot = True,
fmt = ".0f",
linewidth = 0.1,
xticklabels = targets.keys(),
yticklabels = targets.keys())
plt.title("Confusion matrix")
plt.xlabel("Predicted label")
plt.ylabel("True label")
plt.show()
# Show percentage of occurence of all features for all target classes in both train and test data
matrix = []
# Iterate through features
for i, feature in enumerate(features.instances):
# Generate an empty dictionary
x = {}
# Initialize the dictionary with targets as keys and 0 as value
x = x.fromkeys(targets.keys(), 0)
# Count in all train data
for f, d in zip(x_train, train_data):
if f[i] > 0: x[d.annotations["target"]] += 1
# Count in all test data
for f, d in zip(x_test, test_data):
if f[i] > 0: x[d.annotations["target"]] += 1
# Vector of attendance percentage
vector = []
# Iterate through the number of instances and the number of occurrences
for n_instances, n_occurrences in zip(targets.values(), x.values()):
n_instances = min(n_instances, n_samples)
# Compute the percentage of k-mers attendance by target
attendance_percentage = 100 - ((n_instances - n_occurrences) / n_instances * 100)
# Save the attendance percentage in the specitic vector
vector.append(int(attendance_percentage))
# Save the vector of attendance percentage in the heatmap matrix
matrix.append(vector)
# Build the heatmap
fig, ax = plt.subplots(figsize=(15, 20))
sns.heatmap(matrix,
annot = True,
fmt = ".0f",
cmap = 'Blues_r',
linewidth = 0.1,
xticklabels = targets.keys(),
yticklabels = features.instances)
plt.title("Percentage of presence of k-mers according to HIV subtypes")
plt.xlabel("Target")
plt.ylabel("Features")
plt.show()
# For all incorrect records, compute average feature vectors of all correct records for both true and predicted classes
for i_data, i_features in zip(incorrect_data[0:max_incorrect], incorrect_features[0:max_incorrect]):
# Both matrices to plot
true_features = []
pred_features = []
# Iterate through correct records
for c_data, c_features in zip(correct_data, correct_features):
# Compare only if both records are somewhat similar (either same subtype or acgt-percentage range)
#if i_data.annotations["subtype"] == c_data.annotations["subtype"] or i_data.annotations["acgt-percent"] == c_data.annotations["acgt-percent"]:
# If this correct record is in the same class as current incorrect record
if i_data.annotations["target"] == c_data.annotations["target"]:
true_features.append(c_features)
# If this correct record is in the class that the current incorrect record has been predicted to
if i_data.annotations["prediction"] == c_data.annotations["target"]:
pred_features.append(c_features)
# Compute avergare matrices only if similar correct records are found (avoid div per 0)
if len(true_features) != 0 and len(pred_features) != 0:
true_features_mean = np.array(true_features).mean(axis=0)
pred_features_mean = np.array(pred_features).mean(axis=0)
# Build the heatmap
fig, ax = plt.subplots(figsize=(40,5))
sns.heatmap([true_features_mean, i_features, pred_features_mean],
#annot = True,
#fmt = ".0f",
linewidth = 0.1,
cmap = 'Blues',
xticklabels = features.instances,
yticklabels = ["True", "Incorrect", "Prediction"],)
plt.title("Comparaison of incorrect features vector with true and predicted features vectors averages")
plt.xlabel("Features")
plt.show()
# For all incorrect records, compare apparence percentage of all correct records in both true and predicted classes
for i_data, i_features in zip(incorrect_data[0:max_incorrect], incorrect_features[0:max_incorrect]):
# Dictionaries containing nb of occurences of features for all correct records
true_features = {}
pred_features = {}
true_features = true_features.fromkeys(features.instances, 0)
pred_features = pred_features.fromkeys(features.instances, 0)
true_total = 0
pred_total = 0
# Iterate through correct records
for c_data, c_features in zip(correct_data, correct_features):
# Compare only if both records are somewhat similar (either same subtype or acgt-percentage range)
#if i_data.annotations["subtype"] == c_data.annotations["subtype"] or i_data.annotations["acgt-percent"] == c_data.annotations["acgt-percent"]:
# If this correct record is in the same class as current incorrect record
if i_data.annotations["target"] == c_data.annotations["target"]:
true_total += 1
for value, key in zip(c_features, features.instances):
if value > 0: true_features[key] += 1
# If this correct record is in the class that the current incorrect record has been predicted to
if i_data.annotations["prediction"] == c_data.annotations["target"]:
pred_total += 1
for value, key in zip(c_features, features.instances):
if value > 0: pred_features[key] += 1
# Compute avergare matrices only if similar correct records are found (avoid div per 0)
if true_total != 0 and pred_total != 0:
true_vector = list(map((lambda i: i / true_total), true_features.values()))
pred_vector = list(map((lambda i: i / pred_total), pred_features.values()))
# Build the heatmap
fig, ax = plt.subplots(figsize=(40,5))
sns.heatmap([true_vector, i_features, pred_vector],
#annot = True,
#fmt = ".0f",
linewidth = 0.1,
cmap = 'Blues_r',
xticklabels = features.instances,
yticklabels = ["True", "Incorrect", "Prediction"],)
plt.title("Comparaison of incorrect features vector with true and predicted vectors of occurences percents")
plt.xlabel("Features")
plt.show()
# Compute alignement of all incorrect records to all correct record and compute avegarge of scores
print("\nComparison of alignement scores between true and predicted class:")
ids = []
matrix = []
# Used to show progress
progress = ProgressBar(max_value=len(incorrect_data[0:max_incorrect])*len(correct_data[0:max_correct])).start()
count = 0
# Shuffle correct data (when we're sampling it)
shuffle(correct_data)
# Iterate through incorrect data
for i in incorrect_data[0:max_incorrect]:
# Keep different averages for same target class and predicted target class of incorrect record
true_score_sum = 0
true_score_nb = 0
pred_score_sum = 0
pred_score_nb = 0
# Iterate through correct data
for c in correct_data[0:max_correct]:
# Compare only if both records are somewhat in the same category (both same subtype and acgt-percentage range)
#if i.annotations["subtype"] == c.annotations["subtype"] and i.annotations["acgt-percent"] == c.annotations["acgt-percent"]:
# If this correct record is in the same class as current incorrect record
if i.annotations["target"] == c.annotations["target"]:
true_score_sum += pairwise2.align.globalxx(i.seq, c.seq, score_only=True)
true_score_nb += 1
# If this correct record is in the class that the current incorrect record has been predicted to
if i.annotations["prediction"] == c.annotations["target"]:
pred_score_sum += pairwise2.align.globalxx(i.seq, c.seq, score_only=True)
pred_score_nb += 1
# Used to show progress
count += 1
progress.update(count)
# Compute avergare only if similar correct records are found (avoid div per 0)
if true_score_nb != 0 and pred_score_nb != 0:
ids.append(i.id)
matrix.append([true_score_sum/true_score_nb, pred_score_sum/pred_score_nb])
# Normalise results
matrix = pd.DataFrame(np.array(matrix))
matrix = matrix.div(matrix.max(axis=1), axis=0)
# Build the heatmap
fig, ax = plt.subplots()
sns.heatmap(matrix,
#annot = True,
#fmt = ".0f",
linewidth = 0.1,
cmap = 'Blues',
xticklabels = ["True", "Prediction"],
yticklabels = ids)
plt.title("Comparison of alignement scores between true and predicted class")
plt.xlabel("Target")
plt.ylabel("ID")
plt.show()
0% (1 of 10000) | | Elapsed Time: 0:00:03 ETA: 8:37:03 Comparison of alignement scores between true and predicted class: 99% (9998 of 10000) |################## | Elapsed Time: 1:36:41 ETA: 0:00:00
# Tried something, did not work yet...
#features = create(["GGCGG"])
#for i in incorrect_data:
# graphic_features = []
# progress = ProgressBar()
# for pos, seq in progress(features.instances.search(i.seq)):
# graphic_features.append(GraphicFeature(start = pos, end= pos + k, strand = +1, color= "#ffd700", label=str(seq + "\n" + "Position : " + str(pos))))
# record = GraphicRecord(sequence_length = len(i.seq), features=graphic_features)
# record.plot(figure_width = 15)
# plt.title("Sequence : " + i.id)
# plt.show()
#for c in correct_data:
# if c.annotations["target"] == "CRB":
# graphic_features = []
# progress = ProgressBar()
# for pos, seq in progress(features.instances.search(i.seq)):
# graphic_features.append(GraphicFeature(start = pos, end= pos + k, strand = +1, color= "#ffd700", label=str(seq + "\n" + "Position : " + str(pos))))
# record = GraphicRecord(sequence_length = len(i.seq), features=graphic_features)
# record.plot(figure_width = 15)
# plt.title("Sequence : " + c.id)
# plt.show()
# break
#for c in correct_data:
# if c.annotations["target"] == "OCE":
# graphic_features = []
# progress = ProgressBar()
# for pos, seq in progress(features.instances.search(i.seq)):
# graphic_features.append(GraphicFeature(start = pos, end= pos + k, strand = +1, color= "#ffd700", label=str(seq + "\n" + "Position : " + str(pos))))
# record = GraphicRecord(sequence_length = len(i.seq), features=graphic_features)
# record.plot(figure_width = 15)
# plt.title("Sequence : " + c.id)
# plt.show()
# break